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Purpose This paper aims to reveal the tribochemical reaction mechanism on the nano-cutting interface between HMX crystal and diamond tool. Design/methodology/approach Molecular dynamics simulation of HMX crystal nano-cutting by the reactive force field is carried out in this paper. The affinity of activated atoms and friction damage at the different interface have been well identified by comparing two cutting systems with diamond tool or indenter. The analyses of reaction kinetics, decomposition products and reaction pathways are performed to reveal the underlying atomistic origins of tribocatalytic reaction on the nano-cutting interface. Findings The HMX crystals only undergo damage and removal in the indenter cutting, while they appear to accelerate thermal decomposition in the diamond cutting. the C-O affinity is proved to be the intrinsic reason of the tribocatalytic reaction of the HMX-diamond cutting system. The reaction activation energy of the HMX crystals in the diamond cutting system is lower, resulting in a rapid increase in the decomposition degree. The free O atoms can induce the asymmetric ring-opening mode and change the decomposition pathways, which is the underlying atomistic origins of the thermal stability of the HMX-diamond cutting system. Originality/value This paper describes a method for analyzing the tribochemical behavior of HMX and diamond, which is beneficial to study the thermal stability in the nano-cutting of HMX.

Accurately measuring the direction of arrival (DOA) is one of the most important issues in multiple sensor/antenna array monitoring scenarios. However, as a necessary parameter of almost all state-of-the-art DOA estimation methods, the source number is always hard to be determined by using the traditional Akaike information criterion (AIC) or minimum description length (MDL) methods, especially in low or very low signal-to-noise ratio (SNR) conditions. In this paper, we propose to sequentially estimate the SNR and source number in a novel data-driven manner by employing artificial intelligence (AI) techniques. Specifically, a simulated uniform linear array (ULA) dataset with different source numbers and different SNRs is first generated. With this dataset, the artificial neural network (ANN) regression unit for SNR prediction is built. Then, a hierarchical support vector machine (SVM) classification unit is constructed to estimate the source number. Finally, with these hierarchical AI units, the SNR and source number were estimated simultaneously in an iterative way. Experimental results illustrated that the proposed method can estimate the source number stably and reliably even in the low SNR condition.

Today, the quantitative evaluation of the quality of circular or cylindrical workpieces is becoming increasingly important for the relevant industrial production sectors. Although there are already some roundness deviation evaluation algorithms available to accomplish this task, these methods are always done in a holistic way. In many industrial scenarios, however, fine evaluation of the roundness variation of local segments is often more practical than the global assessment. By performing a fine evaluation of roundness variation of local segments, crucial information that can reveal intrinsic quality characteristics of both the workpiece and the production machine can be retrieved. However, this important issue has not been well studied. To deal with this problem, a roundness deviation evaluation method based on statistical analysis of local least square circles was proposed. Experimental results illustrated that the proposed method can stably and reliably evaluate the local and global roundness deviations effectively.